The Car-Following Model and Its Applications in the V2X Environment: A Historical Review

Abstract

The application of vehicle-to-everything (V2X) technology has resulted in the traffic environment being different from how it was in the past. In the V2X environment, the information perception ability of the driver–vehicle unit is greatly enhanced. With V2X technology, the driver–vehicle unit can obtain a massive amount of traffic information and is able to form a connection and interaction relationship between multiple vehicles and themselves. In the traditional car-following models, only the dual-vehicle interaction relationship between the object vehicle and its preceding vehicle was considered, making these models unable to be employed to describe the car-following behavior in the V2X environment. As one of the core components of traffic flow theory, research on car-following behavior needs to be further developed. First, the development process of the traditional car-following models is briefly reviewed. Second, previous research on the impacts of V2X technology, car-following models in the V2X environment, and the applications of these models, such as the calibration of the model parameters, the analysis of traffic flow characteristics, and the methods that are used to estimate a vehicle’s energy consumption and emissions, are comprehensively reviewed. Finally, the achievements and shortcomings of these studies along with trends that require further exploration are discussed. The results that were determined here can provide a reference for the further development of traffic flow theory, personalized advanced driving assistance systems, and anthropopathic autonomous-driving vehicles.

Modeling the Car-Following Behavior with Consideration of Driver, Vehicle, and Environment Factors: A Historical Review

“…As pointed out in [5], the car-following models are the theoretical basis of many applications, such as energy consumption and exhaust emission estimation. However, the performance of the previous estimation method is unsatisfactory when describing a vehicle of abnormal type.…”

“…The research on car-following behavior goes back nearly 70 years and covers hundreds of models; it is based on various theories, and different perspectives have been constructed in the developing process. According to the modeling idea, these models can be divided into six types: stimulus-response models, safety distance models, physiology-psychology models, artificial intelligence models, optimal velocity models, and intelligent driving models [1][2][3][4][5]. Among them, the representative models' developing process is shown in Figure 1.…”

Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN

“…In order to support the Advanced Driver Assistance Systems (ADAS), the automobile industry has focused on the research and development of CAVs, particularly Vehicle-to-Everything (V2X) communication. Therefore, most developed countries continue to improve their Intelligent Transportation Systems (ITS) in order to manage people, autonomous vehicles, and roads using cutting-edge technologies [ 2 ]. ITS enable road monitoring, traffic control, vehicular communication, smart decisions during emergency situations, etc.…”

Nonlinear Analysis of the Car-Following Model considering Delay under the V2X Environment

“…Yao et al [20] found that delay time signifcantly impacts intelligent vehicles because vehicles in a platoon run with such a small gap that even a tiny delay can induce platoon instability. Additionally, Han et al [21] conducted corresponding research from macro and micro-perspectives. Hence, theoretical analysis has shown that introducing additional information for the vehicle group ahead can signifcantly improve trafc fow stability.…”